// Copyright (C) 2002, International Business Machines
// Corporation and others.  All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).

#include "CoinPragma.hpp"
#include "ClpSimplex.hpp"
#include "ClpDualRowSteepest.hpp"
#include "CoinIndexedVector.hpp"
#include "ClpFactorization.hpp"
#include "CoinHelperFunctions.hpp"
#include <cstdio>
//#############################################################################
// Constructors / Destructor / Assignment
//#############################################################################
//#define CLP_DEBUG 4
//-------------------------------------------------------------------
// Default Constructor
//-------------------------------------------------------------------
ClpDualRowSteepest::ClpDualRowSteepest(int mode)
  : ClpDualRowPivot()
  , state_(-1)
  , mode_(mode)
  , persistence_(normal)
  , weights_(NULL)
  , infeasible_(NULL)
  , alternateWeights_(NULL)
  , savedWeights_(NULL)
{
  type_ = 2 + 64 * mode;
}

//-------------------------------------------------------------------
// Copy constructor
//-------------------------------------------------------------------
ClpDualRowSteepest::ClpDualRowSteepest(const ClpDualRowSteepest &rhs)
  : ClpDualRowPivot(rhs)
{
  state_ = rhs.state_;
  mode_ = rhs.mode_;
  persistence_ = rhs.persistence_;
  model_ = rhs.model_;
  if ((model_ && model_->whatsChanged() & 1) != 0) {
    int number = model_->numberRows();
    if (rhs.savedWeights_)
      number = std::min(number, rhs.savedWeights_->capacity());
    if (rhs.infeasible_) {
      infeasible_ = new CoinIndexedVector(rhs.infeasible_);
    } else {
      infeasible_ = NULL;
    }
    if (rhs.weights_) {
      weights_ = new double[number];
      ClpDisjointCopyN(rhs.weights_, number, weights_);
    } else {
      weights_ = NULL;
    }
    if (rhs.alternateWeights_) {
      alternateWeights_ = new CoinIndexedVector(rhs.alternateWeights_);
    } else {
      alternateWeights_ = NULL;
    }
    if (rhs.savedWeights_) {
      savedWeights_ = new CoinIndexedVector(rhs.savedWeights_);
    } else {
      savedWeights_ = NULL;
    }
  } else {
    infeasible_ = NULL;
    weights_ = NULL;
    alternateWeights_ = NULL;
    savedWeights_ = NULL;
  }
}

//-------------------------------------------------------------------
// Destructor
//-------------------------------------------------------------------
ClpDualRowSteepest::~ClpDualRowSteepest()
{
  delete[] weights_;
  delete infeasible_;
  delete alternateWeights_;
  delete savedWeights_;
}

//----------------------------------------------------------------
// Assignment operator
//-------------------------------------------------------------------
ClpDualRowSteepest &
ClpDualRowSteepest::operator=(const ClpDualRowSteepest &rhs)
{
  if (this != &rhs) {
    ClpDualRowPivot::operator=(rhs);
    state_ = rhs.state_;
    mode_ = rhs.mode_;
    persistence_ = rhs.persistence_;
    model_ = rhs.model_;
    delete[] weights_;
    delete infeasible_;
    delete alternateWeights_;
    delete savedWeights_;
    assert(model_);
    int number = model_->numberRows();
    if (rhs.savedWeights_)
      number = std::min(number, rhs.savedWeights_->capacity());
    if (rhs.infeasible_ != NULL) {
      infeasible_ = new CoinIndexedVector(rhs.infeasible_);
    } else {
      infeasible_ = NULL;
    }
    if (rhs.weights_ != NULL) {
      weights_ = new double[number];
      ClpDisjointCopyN(rhs.weights_, number, weights_);
    } else {
      weights_ = NULL;
    }
    if (rhs.alternateWeights_ != NULL) {
      alternateWeights_ = new CoinIndexedVector(rhs.alternateWeights_);
    } else {
      alternateWeights_ = NULL;
    }
    if (rhs.savedWeights_ != NULL) {
      savedWeights_ = new CoinIndexedVector(rhs.savedWeights_);
    } else {
      savedWeights_ = NULL;
    }
  }
  return *this;
}
// Fill most values
void ClpDualRowSteepest::fill(const ClpDualRowSteepest &rhs)
{
  state_ = rhs.state_;
  mode_ = rhs.mode_;
  persistence_ = rhs.persistence_;
  assert(model_->numberRows() == rhs.model_->numberRows());
  model_ = rhs.model_;
  assert(model_);
  int number = model_->numberRows();
  if (rhs.savedWeights_)
    number = std::min(number, rhs.savedWeights_->capacity());
  if (rhs.infeasible_ != NULL) {
    if (!infeasible_)
      infeasible_ = new CoinIndexedVector(rhs.infeasible_);
    else
      *infeasible_ = *rhs.infeasible_;
  } else {
    delete infeasible_;
    infeasible_ = NULL;
  }
  if (rhs.weights_ != NULL) {
    if (!weights_)
      weights_ = new double[number];
    ClpDisjointCopyN(rhs.weights_, number, weights_);
  } else {
    delete[] weights_;
    weights_ = NULL;
  }
  if (rhs.alternateWeights_ != NULL) {
    if (!alternateWeights_)
      alternateWeights_ = new CoinIndexedVector(rhs.alternateWeights_);
    else
      *alternateWeights_ = *rhs.alternateWeights_;
  } else {
    delete alternateWeights_;
    alternateWeights_ = NULL;
  }
  if (rhs.savedWeights_ != NULL) {
    if (!savedWeights_)
      savedWeights_ = new CoinIndexedVector(rhs.savedWeights_);
    else
      *savedWeights_ = *rhs.savedWeights_;
  } else {
    delete savedWeights_;
    savedWeights_ = NULL;
  }
}
// Returns pivot row, -1 if none
int ClpDualRowSteepest::pivotRow()
{
  assert(model_);
  int i, iRow;
  double *infeas = infeasible_->denseVector();
  double largest = 0.0;
  int *index = infeasible_->getIndices();
  int number = infeasible_->getNumElements();
  const int *pivotVariable = model_->pivotVariable();
  int chosenRow = -1;
  int lastPivotRow = model_->pivotRow();
  assert(lastPivotRow < model_->numberRows());
  double tolerance = model_->currentPrimalTolerance();
  // we can't really trust infeasibilities if there is primal error
  // this coding has to mimic coding in checkPrimalSolution
  double error = std::min(1.0e-2, model_->largestPrimalError());
  // allow tolerance at least slightly bigger than standard
  tolerance = tolerance + error;
  // But cap
  tolerance = std::min(1000.0, tolerance);
  tolerance *= tolerance; // as we are using squares
  bool toleranceChanged = false;
  double *solution = model_->solutionRegion();
  double *lower = model_->lowerRegion();
  double *upper = model_->upperRegion();
  // do last pivot row one here
  //#define CLP_DUAL_FIXED_COLUMN_MULTIPLIER 10.0
  if (lastPivotRow >= 0 && lastPivotRow < model_->numberRows()) {
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
    int numberColumns = model_->numberColumns();
#endif
    int iPivot = pivotVariable[lastPivotRow];
    double value = solution[iPivot];
    double lower = model_->lower(iPivot);
    double upper = model_->upper(iPivot);
    if (value > upper + tolerance) {
      value -= upper;
      value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
      if (iPivot < numberColumns)
        value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
      // store square in list
      if (infeas[lastPivotRow])
        infeas[lastPivotRow] = value; // already there
      else
        infeasible_->quickAdd(lastPivotRow, value);
    } else if (value < lower - tolerance) {
      value -= lower;
      value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
      if (iPivot < numberColumns)
        value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
      // store square in list
      if (infeas[lastPivotRow])
        infeas[lastPivotRow] = value; // already there
      else
        infeasible_->add(lastPivotRow, value);
    } else {
      // feasible - was it infeasible - if so set tiny
      if (infeas[lastPivotRow])
        infeas[lastPivotRow] = COIN_INDEXED_REALLY_TINY_ELEMENT;
    }
    number = infeasible_->getNumElements();
  }
  if (model_->numberIterations() < model_->lastBadIteration() + 200) {
    // we can't really trust infeasibilities if there is dual error
    if (model_->largestDualError() > model_->largestPrimalError()) {
      tolerance *= std::min(model_->largestDualError() / model_->largestPrimalError(), 1000.0);
      toleranceChanged = true;
    }
  }
  int numberWanted;
  if (mode_ < 2) {
    numberWanted = number + 1;
  } else if (mode_ == 2) {
    numberWanted = std::max(2000, number / 8);
  } else {
    int numberElements = model_->factorization()->numberElements();
    double ratio = static_cast< double >(numberElements) / static_cast< double >(model_->numberRows());
    numberWanted = std::max(2000, number / 8);
    if (ratio < 1.0) {
      numberWanted = std::max(2000, number / 20);
    } else if (ratio > 10.0) {
      ratio = number * (ratio / 80.0);
      if (ratio > number)
        numberWanted = number + 1;
      else
        numberWanted = std::max(2000, static_cast< int >(ratio));
    }
  }
  if (model_->largestPrimalError() > 1.0e-3)
    numberWanted = number + 1; // be safe
  int iPass;
  // Setup two passes
  int start[4];
  start[1] = number;
  start[2] = 0;
  double dstart = static_cast< double >(number) * model_->randomNumberGenerator()->randomDouble();
  start[0] = static_cast< int >(dstart);
  start[3] = start[0];
  //double largestWeight=0.0;
  //double smallestWeight=1.0e100;
  for (iPass = 0; iPass < 2; iPass++) {
    int end = start[2 * iPass + 1];
    for (i = start[2 * iPass]; i < end; i++) {
      iRow = index[i];
      double value = infeas[iRow];
      if (value > tolerance) {
        //#define OUT_EQ
#ifdef OUT_EQ
        {
          int iSequence = pivotVariable[iRow];
          if (upper[iSequence] == lower[iSequence])
            value *= 2.0;
        }
#endif
        double weight = std::min(weights_[iRow], 1.0e50);
        //largestWeight = std::max(largestWeight,weight);
        //smallestWeight = std::min(smallestWeight,weight);
        if (value > largest * weight) {
          // make last pivot row last resort choice
          if (iRow == lastPivotRow) {
            if (value * 1.0e-10 < largest * weight)
              continue;
            else
              value *= 1.0e-10;
          }
          int iSequence = pivotVariable[iRow];
          if (!model_->flagged(iSequence)) {
            //#define CLP_DEBUG 3
#ifdef CLP_DEBUG
            double value2 = 0.0;
            if (solution[iSequence] > upper[iSequence] + tolerance)
              value2 = solution[iSequence] - upper[iSequence];
            else if (solution[iSequence] < lower[iSequence] - tolerance)
              value2 = solution[iSequence] - lower[iSequence];
            assert(fabs(value2 * value2 - infeas[iRow]) < 1.0e-8 * std::min(value2 * value2, infeas[iRow]));
#endif
            if (solution[iSequence] > upper[iSequence] + tolerance || solution[iSequence] < lower[iSequence] - tolerance) {
              chosenRow = iRow;
              largest = value / weight;
              //largestWeight = dubious;
            }
          } else {
            // just to make sure we don't exit before got something
            numberWanted++;
          }
        }
        numberWanted--;
        if (!numberWanted)
          break;
      }
    }
    if (!numberWanted)
      break;
  }
  //printf("smallest %g largest %g\n",smallestWeight,largestWeight);
  if (chosenRow < 0 && toleranceChanged) {
    // won't line up with checkPrimalSolution - do again
    double saveError = model_->largestDualError();
    model_->setLargestDualError(0.0);
    // can't loop
    chosenRow = pivotRow();
    number = infeasible_->getNumElements();
    model_->setLargestDualError(saveError);
  }
  if (chosenRow < 0 && lastPivotRow < 0) {
    int nLeft = 0;
    for (int i = 0; i < number; i++) {
      int iRow = index[i];
      if (fabs(infeas[iRow]) > 1.0e-50) {
        index[nLeft++] = iRow;
      } else {
        infeas[iRow] = 0.0;
      }
    }
    infeasible_->setNumElements(nLeft);
    model_->setNumberPrimalInfeasibilities(nLeft);
  }
#ifndef NDEBUG
  //infeasible_->checkClean();
#endif
  return chosenRow;
}
#if 0
static double ft_count = 0.0;
static double up_count = 0.0;
static double ft_count_in = 0.0;
static double up_count_in = 0.0;
static int xx_count = 0;
#endif
/* Updates weights and returns pivot alpha.
   Also does FT update */
double
ClpDualRowSteepest::updateWeights(CoinIndexedVector *input,
  CoinIndexedVector *spare,
  CoinIndexedVector *spare2,
  CoinIndexedVector *updatedColumn)
{
  //#define CLP_DEBUG 3
#if CLP_DEBUG > 2
  // Very expensive debug
  {
    int numberRows = model_->numberRows();
    CoinIndexedVector *temp = new CoinIndexedVector();
    temp->reserve(numberRows + model_->factorization()->maximumPivots());
    double *array = alternateWeights_->denseVector();
    int *which = alternateWeights_->getIndices();
    alternateWeights_->clear();
    int i;
    for (i = 0; i < numberRows; i++) {
      double value = 0.0;
      array[i] = 1.0;
      which[0] = i;
      alternateWeights_->setNumElements(1);
      model_->factorization()->updateColumnTranspose(temp,
        alternateWeights_);
      int number = alternateWeights_->getNumElements();
      int j;
      for (j = 0; j < number; j++) {
        int iRow = which[j];
        value += array[iRow] * array[iRow];
        array[iRow] = 0.0;
      }
      alternateWeights_->setNumElements(0);
      double w = std::max(weights_[i], value) * .1;
      if (fabs(weights_[i] - value) > w) {
        printf("%d old %g, true %g\n", i, weights_[i], value);
        weights_[i] = value; // to reduce printout
      }
      //else
      //printf("%d matches %g\n",i,value);
    }
    delete temp;
  }
#endif
  assert(input->packedMode());
  if (!updatedColumn->packedMode()) {
    // I think this means empty
#ifdef COIN_DEVELOP
    printf("updatedColumn not packed mode ClpDualRowSteepest::updateWeights\n");
#endif
    return 0.0;
  }
  double alpha = 0.0;
  if (!model_->factorization()->networkBasis()) {
    // clear other region
    alternateWeights_->clear();
    double norm = 0.0;
    int i;
    double *work = input->denseVector();
    int numberNonZero = input->getNumElements();
    int *which = input->getIndices();
    double *work2 = spare->denseVector();
    int *which2 = spare->getIndices();
    // ftran
    //permute and move indices into index array
    //also compute norm
    //int *regionIndex = alternateWeights_->getIndices (  );
    const int *permute = model_->factorization()->permute();
    //double * region = alternateWeights_->denseVector();
    if (permute) {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = which[i];
        double value = work[i];
        norm += value * value;
        iRow = permute[iRow];
        work2[iRow] = value;
        which2[i] = iRow;
      }
    } else {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = which[i];
        double value = work[i];
        norm += value * value;
        //iRow = permute[iRow];
        work2[iRow] = value;
        which2[i] = iRow;
      }
    }
    spare->setNumElements(numberNonZero);
    // Do FT update
#if 0
          ft_count_in += updatedColumn->getNumElements();
          up_count_in += spare->getNumElements();
#endif
    if (permute || true) {
#if CLP_DEBUG > 2
      printf("REGION before %d els\n", spare->getNumElements());
      spare->print();
#endif
      model_->factorization()->updateTwoColumnsFT(spare2, updatedColumn,
        spare, permute != NULL);
#if CLP_DEBUG > 2
      printf("REGION after %d els\n", spare->getNumElements());
      spare->print();
#endif
    } else {
      // Leave as old way
      model_->factorization()->updateColumnFT(spare2, updatedColumn);
      model_->factorization()->updateColumn(spare2, spare, false);
    }
#undef CLP_DEBUG
#if 0
          ft_count += updatedColumn->getNumElements();
          up_count += spare->getNumElements();
          xx_count++;
          if ((xx_count % 1000) == 0)
               printf("zz %d ft %g up %g (in %g %g)\n", xx_count, ft_count, up_count,
                      ft_count_in, up_count_in);
#endif
    numberNonZero = spare->getNumElements();
    // alternateWeights_ should still be empty
    int pivotRow = model_->pivotRow();
#ifdef CLP_DEBUG
    if (model_->logLevel() > 4 && fabs(norm - weights_[pivotRow]) > 1.0e-3 * (1.0 + norm))
      printf("on row %d, true weight %g, old %g\n",
        pivotRow, sqrt(norm), sqrt(weights_[pivotRow]));
#endif
    // could re-initialize here (could be expensive)
    norm /= model_->alpha() * model_->alpha();
    assert(model_->alpha());
    assert(norm);
    // pivot element
    alpha = 0.0;
    double multiplier = 2.0 / model_->alpha();
    // look at updated column
    work = updatedColumn->denseVector();
    numberNonZero = updatedColumn->getNumElements();
    which = updatedColumn->getIndices();

    int nSave = 0;
    double *work3 = alternateWeights_->denseVector();
    int *which3 = alternateWeights_->getIndices();
    const int *pivotColumn = model_->factorization()->pivotColumn();
    for (i = 0; i < numberNonZero; i++) {
      int iRow = which[i];
      double theta = work[i];
      if (iRow == pivotRow)
        alpha = theta;
      double devex = weights_[iRow];
      work3[nSave] = devex; // save old
      which3[nSave++] = iRow;
      // transform to match spare
      int jRow = permute ? pivotColumn[iRow] : iRow;
      double value = work2[jRow];
      devex += theta * (theta * norm + value * multiplier);
      if (devex < DEVEX_TRY_NORM)
        devex = DEVEX_TRY_NORM;
      weights_[iRow] = devex;
    }
    alternateWeights_->setPackedMode(true);
    alternateWeights_->setNumElements(nSave);
    if (norm < DEVEX_TRY_NORM)
      norm = DEVEX_TRY_NORM;
    // Try this to make less likely will happen again and stop cycling
    //norm *= 1.02;
    weights_[pivotRow] = norm;
    spare->clear();
#ifdef CLP_DEBUG
    spare->checkClear();
#endif
  } else {
    // Do FT update
    model_->factorization()->updateColumnFT(spare, updatedColumn);
    // clear other region
    alternateWeights_->clear();
    double norm = 0.0;
    int i;
    double *work = input->denseVector();
    int number = input->getNumElements();
    int *which = input->getIndices();
    double *work2 = spare->denseVector();
    int *which2 = spare->getIndices();
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      double value = work[i];
      norm += value * value;
      work2[iRow] = value;
      which2[i] = iRow;
    }
    spare->setNumElements(number);
    // ftran
#ifndef NDEBUG
    alternateWeights_->checkClear();
#endif
    model_->factorization()->updateColumn(alternateWeights_, spare);
    // alternateWeights_ should still be empty
#ifndef NDEBUG
    alternateWeights_->checkClear();
#endif
    int pivotRow = model_->pivotRow();
#ifdef CLP_DEBUG
    if (model_->logLevel() > 4 && fabs(norm - weights_[pivotRow]) > 1.0e-3 * (1.0 + norm))
      printf("on row %d, true weight %g, old %g\n",
        pivotRow, sqrt(norm), sqrt(weights_[pivotRow]));
#endif
    // could re-initialize here (could be expensive)
    norm /= model_->alpha() * model_->alpha();

    assert(norm);
    //if (norm < DEVEX_TRY_NORM)
    //norm = DEVEX_TRY_NORM;
    // pivot element
    alpha = 0.0;
    double multiplier = 2.0 / model_->alpha();
    // look at updated column
    work = updatedColumn->denseVector();
    number = updatedColumn->getNumElements();
    which = updatedColumn->getIndices();

    int nSave = 0;
    double *work3 = alternateWeights_->denseVector();
    int *which3 = alternateWeights_->getIndices();
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      double theta = work[i];
      if (iRow == pivotRow)
        alpha = theta;
      double devex = weights_[iRow];
      work3[nSave] = devex; // save old
      which3[nSave++] = iRow;
      double value = work2[iRow];
      devex += theta * (theta * norm + value * multiplier);
      if (devex < DEVEX_TRY_NORM)
        devex = DEVEX_TRY_NORM;
      weights_[iRow] = devex;
    }
    if (!alpha) {
      // error - but carry on
      alpha = 1.0e-50;
    }
    alternateWeights_->setPackedMode(true);
    alternateWeights_->setNumElements(nSave);
    if (norm < DEVEX_TRY_NORM)
      norm = DEVEX_TRY_NORM;
    weights_[pivotRow] = norm;
    spare->clear();
  }
#ifdef CLP_DEBUG
  spare->checkClear();
#endif
  return alpha;
}

/* Updates primal solution (and maybe list of candidates)
   Uses input vector which it deletes
   Computes change in objective function
*/
void ClpDualRowSteepest::updatePrimalSolution(
  CoinIndexedVector *primalUpdate,
  double primalRatio,
  double &objectiveChange)
{
  double *COIN_RESTRICT work = primalUpdate->denseVector();
  int number = primalUpdate->getNumElements();
  int *COIN_RESTRICT which = primalUpdate->getIndices();
  int i;
  double changeObj = 0.0;
  double tolerance = model_->currentPrimalTolerance();
  const int *COIN_RESTRICT pivotVariable = model_->pivotVariable();
  double *COIN_RESTRICT infeas = infeasible_->denseVector();
  double *COIN_RESTRICT solution = model_->solutionRegion();
  const double *COIN_RESTRICT costModel = model_->costRegion();
  const double *COIN_RESTRICT lowerModel = model_->lowerRegion();
  const double *COIN_RESTRICT upperModel = model_->upperRegion();
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
  int numberColumns = model_->numberColumns();
#endif
  if (primalUpdate->packedMode()) {
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      int iPivot = pivotVariable[iRow];
      double value = solution[iPivot];
      double cost = costModel[iPivot];
      double change = primalRatio * work[i];
      work[i] = 0.0;
      value -= change;
      changeObj -= change * cost;
      double lower = lowerModel[iPivot];
      double upper = upperModel[iPivot];
      solution[iPivot] = value;
      if (value < lower - tolerance) {
        value -= lower;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        if (infeas[iRow])
          infeas[iRow] = value; // already there
        else
          infeasible_->quickAdd(iRow, value);
      } else if (value > upper + tolerance) {
        value -= upper;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        if (infeas[iRow])
          infeas[iRow] = value; // already there
        else
          infeasible_->quickAdd(iRow, value);
      } else {
        // feasible - was it infeasible - if so set tiny
        if (infeas[iRow])
          infeas[iRow] = COIN_INDEXED_REALLY_TINY_ELEMENT;
      }
    }
  } else {
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      int iPivot = pivotVariable[iRow];
      double value = solution[iPivot];
      double cost = costModel[iPivot];
      double change = primalRatio * work[iRow];
      value -= change;
      changeObj -= change * cost;
      double lower = lowerModel[iPivot];
      double upper = upperModel[iPivot];
      solution[iPivot] = value;
      if (value < lower - tolerance) {
        value -= lower;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        if (infeas[iRow])
          infeas[iRow] = value; // already there
        else
          infeasible_->quickAdd(iRow, value);
      } else if (value > upper + tolerance) {
        value -= upper;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        if (infeas[iRow])
          infeas[iRow] = value; // already there
        else
          infeasible_->quickAdd(iRow, value);
      } else {
        // feasible - was it infeasible - if so set tiny
        if (infeas[iRow])
          infeas[iRow] = COIN_INDEXED_REALLY_TINY_ELEMENT;
      }
      work[iRow] = 0.0;
    }
  }
  // Do pivot row
  {
    int iRow = model_->pivotRow();
    // feasible - was it infeasible - if so set tiny
    //assert (infeas[iRow]);
    if (infeas[iRow])
      infeas[iRow] = COIN_INDEXED_REALLY_TINY_ELEMENT;
  }
  primalUpdate->setNumElements(0);
  objectiveChange += changeObj;
}
/* Saves any weights round factorization as pivot rows may change
   1) before factorization
   2) after factorization
   3) just redo infeasibilities
   4) as 2 but restore weights from previous snapshot
   5) for strong branching - initialize to 1 , infeasibilities
   6) scale back
   7) for strong branching - initialize full weights , infeasibilities
*/
void ClpDualRowSteepest::saveWeights(ClpSimplex *model, int mode)
{
  // alternateWeights_ is defined as indexed but is treated oddly
  model_ = model;
  int numberRows = model_->numberRows();
  int numberColumns = model_->numberColumns();
  const int *pivotVariable = model_->pivotVariable();
  int i;
  if (mode == 1) {
    if (weights_) {
      // Check if size has changed
      if (infeasible_->capacity() == numberRows) {
        alternateWeights_->clear();
        // change from row numbers to sequence numbers
        int *which = alternateWeights_->getIndices();
        for (i = 0; i < numberRows; i++) {
          int iPivot = pivotVariable[i];
          which[i] = iPivot;
        }
        state_ = 1;
        // clear marker on savedWeights_
        assert(savedWeights_);
        if (savedWeights_->packedMode()) {
          savedWeights_->setPackedMode(false);
          savedWeights_->setNumElements(0);
        }
      } else {
        // size has changed - clear everything
        delete[] weights_;
        weights_ = NULL;
        delete infeasible_;
        infeasible_ = NULL;
        delete alternateWeights_;
        alternateWeights_ = NULL;
        delete savedWeights_;
        savedWeights_ = NULL;
        state_ = -1;
      }
    }
  } else if (mode == 2 || mode == 4 || mode >= 5) {
    // restore
    if (!weights_ || state_ == -1 || mode == 5 || mode == 7) {
      // initialize weights
      delete[] weights_;
      delete alternateWeights_;
      weights_ = new double[numberRows];
      // initialize to 1.0 (can we do better?)
      for (i = 0; i < numberRows; i++) {
        weights_[i] = 1.0;
      }
      alternateWeights_ = new CoinIndexedVector();
      // enough space so can use it for factorization
      alternateWeights_->reserve(numberRows + model_->factorization()->maximumPivots());
      if (mode_ != 1 || mode == 5) {
      } else {
        CoinIndexedVector *temp = new CoinIndexedVector();
        temp->reserve(numberRows + model_->factorization()->maximumPivots());
        double *array = alternateWeights_->denseVector();
        int *which = alternateWeights_->getIndices();
        int firstRow = 0;
        int lastRow = numberRows;
        if (mode == 7) {
          // use info passed in
          firstRow = model->spareIntArray_[0];
          lastRow = model->spareIntArray_[1];
        }
        for (i = firstRow; i < lastRow; i++) {
          double value = 0.0;
          array[0] = 1.0;
          which[0] = i;
          alternateWeights_->setNumElements(1);
          alternateWeights_->setPackedMode(true);
          model_->factorization()->updateColumnTranspose(temp,
            alternateWeights_);
          int number = alternateWeights_->getNumElements();
          int j;
          for (j = 0; j < number; j++) {
            value += array[j] * array[j];
            array[j] = 0.0;
          }
          alternateWeights_->setNumElements(0);
          weights_[i] = value;
        }
        delete temp;
      }
      // create saved weights (not really indexedvector)
      savedWeights_ = new CoinIndexedVector();
      savedWeights_->reserve(numberRows);
      for (int i = 0; i < model_->numberRows(); i++)
        savedWeights_->denseVector()[i] = 1.0;

      double *array = savedWeights_->denseVector();
      int *which = savedWeights_->getIndices();
      for (int i = 0; i < numberRows; i++) {
        array[i] = weights_[i];
        which[i] = pivotVariable[i];
      }
      if (mode == 7) {
        savedWeights_->setNumElements(numberRows); //flag as special
        savedWeights_->setPackedMode(true);
      }
    } else if (mode != 6) {
      int *which = alternateWeights_->getIndices();
      CoinIndexedVector *rowArray3 = model_->rowArray(3);
      rowArray3->clear();
      int *back = rowArray3->getIndices();
      // In case something went wrong
      for (i = 0; i < numberRows + numberColumns; i++)
        back[i] = -1;
      if (mode != 4) {
        // save
        CoinMemcpyN(which, numberRows, savedWeights_->getIndices());
        CoinMemcpyN(weights_, numberRows, savedWeights_->denseVector());
      } else {
        // restore
        //memcpy(which,savedWeights_->getIndices(),
        //     numberRows*sizeof(int));
        //memcpy(weights_,savedWeights_->denseVector(),
        //     numberRows*sizeof(double));
        which = savedWeights_->getIndices();
      }
      // restore (a bit slow - but only every re-factorization)
      double *array = savedWeights_->denseVector();
      for (i = 0; i < numberRows; i++) {
        int iSeq = which[i];
        back[iSeq] = i;
      }
      int firstRow = 0;
      int lastRow = numberRows;
      if (mode == 7) {
        // use info passed in
        firstRow = model->spareIntArray_[0];
        lastRow = model->spareIntArray_[1];
      }
      for (i = firstRow; i < lastRow; i++) {
        int iPivot = pivotVariable[i];
        iPivot = back[iPivot];
        if (iPivot >= 0) {
          weights_[i] = array[iPivot];
          if (weights_[i] < DEVEX_TRY_NORM)
            weights_[i] = DEVEX_TRY_NORM; // may need to check more
        } else {
          // odd
          weights_[i] = 1.0;
          //printf("bad pivot row %d (%d->%d) iPivot %d\n",i,firstRow,lastRow,iPivot);
        }
      }
#if 0
	       printf("mode %d mode_ %d state_ %d\n",mode,mode_,state_);
	       if (!model_->numberIterations()) {
		 for (int k=0;k<numberRows;k+=10)
		   printf("%d %g ",k,weights_[k]);
		 printf("\n");
	       }
#endif
    } else {
      // mode 6 - scale back weights as primal errors
      double primalError = model_->largestPrimalError();
      double allowed;
      if (primalError > 1.0e3)
        allowed = 10.0;
      else if (primalError > 1.0e2)
        allowed = 50.0;
      else if (primalError > 1.0e1)
        allowed = 100.0;
      else
        allowed = 1000.0;
      double allowedInv = 1.0 / allowed;
      for (i = 0; i < numberRows; i++) {
        double value = weights_[i];
        if (value < allowedInv)
          value = allowedInv;
        else if (value > allowed)
          value = allowed;
        weights_[i] = allowed;
      }
    }
    state_ = 0;
    // set up infeasibilities
    if (!infeasible_) {
      infeasible_ = new CoinIndexedVector();
      infeasible_->reserve(numberRows);
    }
  }
  if (mode >= 2) {
    infeasible_->clear();
    int iRow;
    const int *pivotVariable = model_->pivotVariable();
    double tolerance = model_->currentPrimalTolerance();
    for (iRow = 0; iRow < numberRows; iRow++) {
      int iPivot = pivotVariable[iRow];
      double value = model_->solution(iPivot);
      double lower = model_->lower(iPivot);
      double upper = model_->upper(iPivot);
      if (value < lower - tolerance) {
        value -= lower;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        infeasible_->quickAdd(iRow, value);
      } else if (value > upper + tolerance) {
        value -= upper;
        value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
        if (iPivot < numberColumns)
          value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
        if (lower == upper)
          value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
        // store square in list
        infeasible_->quickAdd(iRow, value);
      }
    }
  }
  // see where coming from
  if (mode == 2 && !model->numberIterations()) {
    int options = model->specialOptions();
    if ((options & 16384) != 0 && true) {
      // fast of some sort - could be clever???
      // for now initialize
      if ((options & 524288) != 0 && false) {
        // fathom
        for (int i = 0; i < numberRows; i++)
          weights_[i] = 1.0;
      } else if (true) {
        // strong
        for (int i = 0; i < numberRows; i++)
          weights_[i] = 1.0;
      }
    }
  }
}
// Pass in saved weights
void ClpDualRowSteepest::passInSavedWeights(const CoinIndexedVector *saved)
{
  delete savedWeights_;
  savedWeights_ = new CoinIndexedVector(*saved);
}
// Gets rid of last update
void ClpDualRowSteepest::unrollWeights()
{
  double *saved = alternateWeights_->denseVector();
  int number = alternateWeights_->getNumElements();
  int *which = alternateWeights_->getIndices();
  int i;
  if (alternateWeights_->packedMode()) {
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      weights_[iRow] = saved[i];
      saved[i] = 0.0;
    }
  } else {
    for (i = 0; i < number; i++) {
      int iRow = which[i];
      weights_[iRow] = saved[iRow];
      saved[iRow] = 0.0;
    }
  }
  alternateWeights_->setNumElements(0);
}
//-------------------------------------------------------------------
// Clone
//-------------------------------------------------------------------
ClpDualRowPivot *ClpDualRowSteepest::clone(bool CopyData) const
{
  if (CopyData) {
    return new ClpDualRowSteepest(*this);
  } else {
    return new ClpDualRowSteepest();
  }
}
// Gets rid of all arrays
void ClpDualRowSteepest::clearArrays()
{
  if (persistence_ == normal) {
    delete[] weights_;
    weights_ = NULL;
    delete infeasible_;
    infeasible_ = NULL;
    delete alternateWeights_;
    alternateWeights_ = NULL;
    delete savedWeights_;
    savedWeights_ = NULL;
  }
  state_ = -1;
}
// Returns true if would not find any row
bool ClpDualRowSteepest::looksOptimal() const
{
  int iRow;
  const int *pivotVariable = model_->pivotVariable();
  double tolerance = model_->currentPrimalTolerance();
  // we can't really trust infeasibilities if there is primal error
  // this coding has to mimic coding in checkPrimalSolution
  double error = std::min(1.0e-2, model_->largestPrimalError());
  // allow tolerance at least slightly bigger than standard
  tolerance = tolerance + error;
  // But cap
  tolerance = std::min(1000.0, tolerance);
  int numberRows = model_->numberRows();
  int numberInfeasible = 0;
  for (iRow = 0; iRow < numberRows; iRow++) {
    int iPivot = pivotVariable[iRow];
    double value = model_->solution(iPivot);
    double lower = model_->lower(iPivot);
    double upper = model_->upper(iPivot);
    if (value < lower - tolerance) {
      numberInfeasible++;
    } else if (value > upper + tolerance) {
      numberInfeasible++;
    }
  }
  return (numberInfeasible == 0);
}
// Called when maximum pivots changes
void ClpDualRowSteepest::maximumPivotsChanged()
{
  if (alternateWeights_ && alternateWeights_->capacity() != model_->numberRows() + model_->factorization()->maximumPivots()) {
    delete alternateWeights_;
    alternateWeights_ = new CoinIndexedVector();
    // enough space so can use it for factorization
    alternateWeights_->reserve(model_->numberRows() + model_->factorization()->maximumPivots());
  }
}

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